I
AE
S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI)
Vo
l.
9
,
No
.
3
,
Sep
tem
b
er
2020
,
p
p
.
371
~
378
I
SS
N:
2
2
5
2
-
8938
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ai.
v
9
.i
3
.
p
p
371
-
3
7
8
371
J
o
ur
na
l ho
m
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e
:
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ttp
:
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a
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.
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O
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c dispa
tch
of pow
er genera
tion so
lu
tion usin
g
lig
htning s
ea
rch a
lg
o
rith
m
M
ura
d Ya
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a
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s
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r
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d No
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Un
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rsiti
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u
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Hu
ss
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in
On
n
M
a
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sia
(U
T
H
M
)
,
M
a
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2
De
p
a
rtme
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t
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e
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tri
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Art
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nfo
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ticle
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to
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y:
R
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F
eb
2
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2
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2
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pr
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cc
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ted
Ma
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1
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20
Eco
n
o
m
i
c
d
isp
a
tch
(ED)
is
th
e
p
o
w
e
r
d
e
m
a
n
d
a
ll
o
c
a
ti
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g
p
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o
m
m
it
ted
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it
s
a
t
m
in
i
m
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m
g
e
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e
ra
ti
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n
c
o
st
w
h
il
e
sa
ti
sfy
in
g
s
y
ste
m
a
n
d
o
p
e
ra
ti
o
n
a
l
c
o
n
stra
i
n
ts.
In
c
re
a
sin
g
c
o
st
o
f
f
u
e
l
p
rice
a
n
d
e
lec
tri
c
it
y
d
e
m
a
n
d
c
a
n
in
c
re
a
se
th
e
c
o
st
o
f
th
e
rm
a
l
p
o
w
e
r
g
e
n
e
r
a
ti
o
n
.
T
h
e
re
f
o
re
,
ro
b
u
st
a
n
d
e
ff
ici
e
n
t
o
p
ti
m
iza
ti
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n
a
lg
o
rit
h
m
is
re
q
u
ired
to
d
e
term
in
e
th
e
o
p
ti
m
a
l
so
lu
ti
o
n
f
o
r
ED
p
ro
b
lem
in
p
o
w
e
r
s
y
ste
m
o
p
e
ra
ti
o
n
a
n
d
p
lan
n
i
n
g
.
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th
i
s
p
a
p
e
r
th
e
li
g
h
tn
i
n
g
se
a
rc
h
a
lg
o
rit
h
m
(
L
S
A
)
is
p
ro
p
o
se
d
to
so
lv
e
th
e
ED
p
ro
b
lem
.
T
h
e
s
y
ste
m
c
o
n
stra
in
ts
su
c
h
a
s
p
o
w
e
r
b
a
lan
c
e
,
g
e
n
e
ra
to
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li
m
it
s,
s
y
ste
m
tran
s
m
issio
n
lo
ss
e
s
a
n
d
v
a
lv
e
-
p
o
in
ts
e
ff
e
c
ts
(V
P
E)
a
re
c
o
n
sid
e
re
d
in
th
is
p
a
p
e
r.
T
o
v
e
ri
fy
th
e
e
ff
e
c
t
iv
e
n
e
ss
o
f
L
S
A
in
ter
m
s
o
f
c
o
n
v
e
rg
e
n
c
e
c
h
a
ra
c
teristic,
ro
b
u
stn
e
ss
,
sim
u
latio
n
ti
m
e
a
n
d
so
lu
ti
o
n
q
u
a
li
ty
,
th
e
tw
o
c
a
se
stu
d
ies
c
o
n
sists
o
f
6
a
n
d
1
3
u
n
it
s
h
a
v
e
b
e
e
n
tes
t
e
d
.
T
h
e
si
m
u
lati
o
n
re
su
lt
s
sh
o
w
th
a
t
th
e
L
S
A
c
a
n
p
ro
v
id
e
o
p
ti
m
a
l
c
o
st
th
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n
m
a
n
y
m
e
th
o
d
s
re
p
o
rted
i
n
li
tera
tu
re
.
T
h
e
re
f
o
re
,
it
h
a
s
p
o
ten
ti
a
l
to
so
lv
e
m
a
n
y
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s
in
p
o
w
e
r
d
isp
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tch
a
n
d
p
o
w
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r
s
y
ste
m
a
p
p
li
c
a
ti
o
n
s.
K
ey
w
o
r
d
s
:
E
co
n
o
m
ic
d
is
p
atch
P
o
w
er
s
y
s
te
m
T
r
an
s
m
is
s
io
n
lo
s
s
es
Valv
e
-
p
o
in
ts
lo
ad
i
n
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Mo
h
d
No
o
r
A
b
d
u
llah
,
Gr
ee
n
an
d
S
u
s
tai
n
ab
le
E
n
er
g
y
(
GSEn
er
g
y
)
Fo
cu
s
Gr
o
u
p
,
Facu
lt
y
o
f
E
lectr
ical
a
n
d
E
lect
r
o
n
ic
E
n
g
i
n
ee
r
i
n
g
,
Un
i
v
er
s
iti T
u
n
H
u
s
s
ei
n
On
n
Ma
la
y
s
ia,
P
ar
it R
aj
a,
8
6
0
0
0
B
atu
P
ah
at,
J
o
h
o
r
,
Ma
lay
s
ia.
E
m
ail:
m
n
o
o
r
@
u
t
h
m
.
e
d
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
Th
e
ec
o
n
o
m
ic
d
is
p
atc
h
(
ED
)
is
o
n
e
o
f
t
h
e
o
p
ti
m
izatio
n
p
r
o
b
lem
s
i
n
p
o
w
er
s
y
s
te
m
o
p
e
r
atio
n
an
d
p
lan
n
i
n
g
to
allo
ca
te
th
e
s
h
ar
ed
p
o
w
er
d
e
m
a
n
d
b
et
w
ee
n
t
h
e
g
e
n
er
atin
g
u
n
i
ts
.
T
h
u
s
,
o
p
ti
m
al
p
o
w
er
s
y
s
te
m
o
p
er
atio
n
is
i
m
p
o
r
ta
n
t
i
n
elec
t
r
ical
n
e
t
w
o
r
k
s
to
e
n
s
u
r
e
t
h
e
s
y
s
te
m
ca
n
o
p
er
ate
at
m
i
n
i
m
al
co
s
t.
T
h
er
ef
o
r
e,
th
e
ai
m
o
f
t
h
e
E
D
is
to
m
in
i
m
ize
th
e
to
tal
co
s
t o
f
g
e
n
er
atio
n
an
d
s
atis
f
y
t
h
e
s
y
s
te
m
a
n
d
o
p
er
atin
g
co
n
s
tr
ai
n
ts
[
1
]
.
V
ar
io
u
s
o
p
ti
m
iza
tio
n
s
m
et
h
o
d
s
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
an
d
ap
p
lied
to
s
o
lv
e
ED
p
r
o
b
lem
o
v
er
th
e
lates
t
d
ec
ad
es
an
d
ca
n
b
e
class
if
ied
in
to
t
w
o
m
a
in
ca
te
g
o
r
ies
s
u
c
h
as
cla
s
s
ical
m
et
h
o
d
an
d
h
e
u
r
is
tic
m
et
h
o
d
[
2
]
.
T
h
e
class
ical
m
et
h
o
d
s
s
u
c
h
as
n
e
w
to
n
'
s
m
et
h
o
d
[
3
]
,
q
u
ad
r
at
ic
p
r
o
g
r
am
m
i
n
g
tec
h
n
iq
u
e
[
4
]
,
in
ter
io
r
p
o
in
t
[
5
]
,
la
m
b
d
a
iter
atio
n
m
et
h
o
d
[
6
]
,
ev
o
lu
tio
n
ar
y
p
r
o
g
r
a
m
m
i
n
g
(
E
P
)
tech
n
iq
u
es
[
7
]
an
d
d
y
n
a
m
i
c
p
r
o
g
r
am
m
i
n
g
[
8
]
ar
e
w
id
el
y
u
s
ed
f
o
r
s
o
lv
i
n
g
co
n
v
e
x
a
n
d
s
m
o
o
t
h
co
s
t
f
u
n
ctio
n
o
f
E
D
p
r
o
b
le
m
.
Ho
w
e
v
er
,
m
o
s
t
o
f
th
e
s
e
m
et
h
o
d
s
h
a
v
e
d
i
f
f
ic
u
lt
y
f
o
r
s
o
lv
i
n
g
n
o
n
co
n
v
e
x
o
r
n
o
n
s
m
o
o
t
h
p
r
o
b
le
m
s
.
T
o
s
o
lv
e
t
h
is
p
r
o
b
le
m
,
th
e
n
o
n
-
co
n
v
en
tio
n
al
o
r
h
e
u
r
is
tic
m
et
h
o
d
s
ar
e
d
ev
elo
p
ed
to
s
o
lv
e
t
h
e
co
m
p
licated
an
d
h
i
g
h
l
y
n
o
n
co
n
v
e
x
o
p
tim
izatio
n
p
r
o
b
lem
.
T
h
e
o
p
ti
m
izat
io
n
al
g
o
r
ith
m
s
s
u
c
h
as
an
t
co
lo
n
y
o
p
ti
m
izatio
n
(
A
C
O)
[
9
]
,
ar
tif
icial
b
ee
co
lo
n
y
(
A
B
C
)
[
1
0
]
,
f
ir
ef
l
y
a
l
g
o
r
ith
m
(
F
A
)
[
1
1
]
,
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
P
SO)
[
1
2
]
,
teac
h
in
g
–
lear
n
i
n
g
-
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
3
,
Sep
te
m
b
er
20
20
:
3
7
1
–
378
372
b
ased
o
p
ti
m
izatio
n
(
T
L
B
O)
[
13]
,
g
en
etic
al
g
o
r
it
h
m
(
G
A
)
[
1
4
]
an
d
ad
ap
tiv
e
c
h
ar
g
e
d
s
y
s
t
e
m
s
ea
r
ch
a
lg
o
r
it
h
m
(
AC
SS
)
[
1
5
]
h
a
v
e
b
ee
n
s
o
lv
e
d
th
e
co
m
p
lex
E
D
p
r
o
b
lem
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
s
e
al
g
o
r
ith
m
s
is
b
etter
t
h
a
n
class
ical
o
p
ti
m
izat
io
n
m
et
h
o
d
s
i
n
m
a
n
y
asp
ec
ts
f
o
r
i
n
s
tan
c
e
f
a
s
t,
r
o
b
u
s
t
an
d
ea
s
y
to
ad
j
u
s
t
ac
co
r
d
in
g
to
t
h
e
p
r
o
b
lem
.
I
n
s
o
m
e
ap
p
licatio
n
,
th
ese
al
g
o
r
ith
m
s
ar
e
s
u
f
f
e
r
in
g
f
r
o
m
s
lo
w
co
n
v
er
g
e
n
ce
r
ate,
s
tu
ck
at
lo
ca
l
s
o
lu
tio
n
an
d
r
eq
u
ir
ed
p
r
o
p
er
p
ar
a
m
eter
tu
n
i
n
g
to
o
b
tain
o
p
ti
m
al
s
o
l
u
tio
n
.
Fu
r
t
h
er
m
o
r
e,
th
e
h
y
b
r
id
m
et
h
o
d
h
as
b
ee
n
in
tr
o
d
u
ce
d
b
y
co
m
b
i
n
i
n
g
t
w
o
o
r
m
o
r
e
alg
o
r
ith
m
s
in
o
r
d
er
to
m
it
ig
ate
t
h
e
ir
w
ea
k
n
e
s
s
e
s
a
n
d
u
s
e
t
h
eir
s
tr
en
g
th
s
to
p
r
o
v
id
e
b
etter
p
er
f
o
r
m
an
ce
f
o
r
s
o
lv
i
n
g
o
p
tim
izatio
n
p
r
o
b
le
m
s
[
1
6
]
.
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
i
d
alg
o
r
ith
m
s
s
u
ch
a
s
G
A
-
PS
-
SQP
[
1
7
]
,
NM
-
F
A
P
SO
[1
8]
an
d
d
if
f
er
e
n
tial
e
v
o
lu
tio
n
alg
o
r
ith
m
-
P
SO
[
1
9
]
s
h
o
w
s
h
i
g
h
l
y
e
f
f
icie
n
t
tech
n
iq
u
e
to
s
o
lv
e
th
e
E
D
p
r
o
b
lem
.
Ho
w
e
v
er
,
it
r
eq
u
ir
ed
l
o
n
g
co
m
p
u
tatio
n
al
ti
m
e
a
n
d
co
m
p
lex
p
r
o
g
r
a
m
m
i
n
g
s
i
n
ce
t
w
o
o
r
m
o
r
e
alg
o
r
ith
m
s
ar
e
u
s
ed
.
R
ec
en
t
l
y
,
a
n
e
w
n
o
n
co
n
v
e
n
ti
o
n
al
alg
o
r
it
h
m
ca
lled
th
e
L
S
A
al
g
o
r
ith
m
[
2
0
]
h
as
n
e
v
er
a
p
p
lied
f
o
r
s
o
lv
i
n
g
E
D
p
r
o
b
le
m
.
I
t
w
as
s
u
cc
ess
f
u
l
l
y
u
s
ed
an
d
i
m
p
le
m
e
n
ted
to
o
p
ti
m
ize
th
e
d
i
f
f
er
e
n
t
a
p
p
licatio
n
s
s
u
ch
as
b
in
ar
y
o
p
ti
m
izat
io
n
[
2
1
]
,
n
u
clea
r
r
ea
cto
r
c
o
n
tr
o
ller
[
2
2
]
,
an
d
Fu
zz
y
L
o
g
ic
P
V
I
n
v
er
t
er
C
o
n
tr
o
ller
[
2
3
]
.
I
t
s
h
o
w
s
th
e
L
S
A
ca
n
p
r
o
v
id
e
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
o
v
er
o
th
er
alg
o
r
it
h
m
s
.
T
h
er
ef
o
r
e,
th
is
p
ap
er
p
r
o
p
o
s
ed
L
S
A
a
s
a
n
e
w
ap
p
r
o
ac
h
f
o
r
s
o
lv
i
n
g
E
D
p
r
o
b
le
m
s
w
i
t
h
VP
.
T
h
e
p
er
f
o
r
m
an
ce
o
f
L
S
A
h
a
s
b
ee
n
co
m
p
ar
ed
w
it
h
o
th
er
co
m
m
o
n
m
et
h
o
d
s
.
T
h
e
r
e
m
ain
in
g
o
f
th
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
:
Secti
o
n
2
p
r
o
v
id
es
th
e
Ma
th
e
m
atica
l
f
o
r
m
u
latio
n
o
f
E
D
p
r
o
b
lem
co
n
s
id
er
in
g
VP
E
an
d
lo
s
s
e
s
,
s
ec
t
io
n
3
p
r
esen
ts
th
e
L
S
A
tec
h
n
iq
u
e,
Sectio
n
4
in
tr
o
d
u
ce
s
t
h
e
s
i
m
u
l
atio
n
r
esu
l
ts
f
o
r
th
e
test
s
y
s
te
m
an
d
it
s
an
al
y
s
i
s
.
2.
P
RO
B
L
E
M
F
O
R
M
UL
AT
I
O
N
O
F
E
CO
NO
M
I
C
DIS
P
AT
CH
(
E
D)
2
.
1
.
O
bje
c
t
iv
e
f
un
ct
io
n
T
h
e
m
ai
n
ai
m
o
f
a
n
y
E
D
p
r
o
b
lem
i
s
m
in
i
m
izin
g
t
h
e
s
y
s
t
e
m
o
p
er
atio
n
co
s
t
in
o
r
d
er
to
f
u
lf
i
ll
th
e
p
o
w
er
d
e
m
an
d
alo
n
g
s
id
e
th
e
g
en
er
ato
r
li
m
it
s
.
T
h
e
f
u
e
l
co
s
t
f
u
n
ct
io
n
ca
n
b
e
d
if
f
er
e
n
tiated
a
s
a
s
i
m
p
l
if
ied
co
s
t
f
u
n
ctio
n
a
n
d
m
o
d
if
ied
f
u
n
c
ti
o
n
b
y
i
n
cl
u
d
in
g
w
it
h
VP
E
.
T
h
e
co
s
t
f
u
n
ct
io
n
o
f
p
r
o
d
u
ctio
n
u
n
it
s
ca
n
b
e
r
ep
r
esen
ted
b
y
a
q
u
ad
r
atic
f
u
n
ctio
n
as f
o
llo
w
s
:
1
g
N
i
G
G
i
i
F
P
F
P
(
1
)
2
iii
i
G
i
G
i
G
i
F
P
a
P
b
P
c
(
2
)
C
o
n
s
id
er
in
g
VP
E
f
o
r
E
D
p
r
o
b
lem
i
n
th
e
f
u
el
co
s
t
f
u
n
ct
io
n
s
ex
h
ib
it
a
b
ig
g
er
v
ar
iati
o
n
in
th
e
g
en
er
ati
n
g
u
n
its
w
it
h
m
u
lti
-
v
alv
e
s
tea
m
t
u
r
b
in
es.
T
h
e
g
e
n
er
ated
p
o
w
er
w
ill
b
e
c
h
a
n
g
ed
w
h
e
n
e
v
er
y
s
tea
m
v
alv
e
clo
s
e
s
o
r
o
p
en
s
.
I
n
th
e
cu
r
v
e
s
o
f
th
e
h
ea
t
r
atio
in
tr
o
d
u
ce
s
r
ip
p
les
b
y
VP
E
.
T
h
e
co
s
t
f
u
n
ct
io
n
o
f
E
D
w
ill
ad
d
a
s
in
u
s
o
id
al
ter
m
a
n
d
it c
a
n
b
e
d
ef
in
ed
m
ath
e
m
atica
ll
y
a
s
:
2
b
s
i
n
i
i
i
i
G
i
G
i
i
i
G
i
m
i
n
G
i
F
P
a
P
c
P
e
F
P
P
(
3
)
w
h
er
e
F
(
P
G
)
is
th
e
to
tal
co
s
t
o
f
p
r
o
d
u
ctio
n
,
F
(
P
Gi
)
i
s
t
h
e
u
n
it
f
u
n
c
tio
n
o
f
f
u
el
co
s
t
i
;
,
,
,
,
an
d
ar
e
t
h
e
u
n
i
t c
o
ef
f
icie
n
ts
o
f
f
u
el
co
s
t
i
;
P
Gi
is
th
e
u
n
i
t o
u
tp
u
t
i
o
f
r
ea
l p
o
w
er
.
2.
2
.
Sy
s
t
em
o
pera
t
io
n c
o
ns
t
ra
ints
I
t sh
o
u
ld
s
a
tis
f
y
th
e
f
o
llo
w
i
n
g
s
y
s
te
m
co
n
s
tr
ai
n
t
s
w
h
e
n
E
D
Op
ti
m
izatio
n
ca
r
r
ied
o
u
t.
T
h
e
co
n
s
tr
ain
ts
co
n
s
id
er
ed
f
o
r
th
is
r
esear
c
h
ar
e
d
is
cu
s
s
ed
in
s
ec
tio
n
s
2
.
2
.
1
an
d
2
.
2
.
2
.
2
.
2
.
1
.
E
qu
a
lity
co
ns
t
ra
int
T
h
e
eq
u
alit
y
co
n
s
tr
ain
t
is
r
ep
r
esen
ted
t
h
e
p
o
w
er
b
ala
n
ce
eq
u
atio
n
an
d
it
i
s
u
s
ed
in
th
e
o
p
ti
m
izat
io
n
to
en
s
u
r
e
th
a
t
th
e
to
tal
p
o
w
er
g
en
er
ated
m
u
s
t
b
e
eq
u
al
to
th
e
p
o
w
er
d
e
m
a
n
d
in
t
h
e
ca
s
e
w
it
h
o
u
t
lo
s
s
e
s
i
n
t
h
e
s
y
s
te
m
ca
n
b
e
w
r
i
tten
a
s
:
1
n
iD
i
PP
(
4
)
I
f
co
n
s
id
er
p
o
w
er
lo
s
s
e
s
,
to
tal
g
en
er
ated
p
o
w
er
m
u
s
t b
e
eq
u
a
l p
o
w
er
lo
s
s
es a
n
d
p
o
w
er
d
e
m
an
d
as f
o
llo
w
s
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J
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r
ti
f
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n
tell
I
SS
N:
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tima
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tio
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373
1
n
i
L
D
i
P
P
P
(
5
)
w
h
er
e
P
i
is
th
e
p
o
w
er
g
e
n
e
r
at
ed
,
P
L
I
n
d
icate
s
tr
an
s
m
is
s
io
n
lo
s
s
,
P
D
is
th
e
lo
ad
d
e
m
an
d
a
n
d
is
t
h
e
o
v
er
all
a
m
o
u
n
t o
f
t
h
e
g
e
n
er
ati
n
g
u
n
its
.
T
h
e
p
o
w
er
lo
s
s
es c
a
n
b
e
ca
lcu
lated
as:
0
1
0
1
0
1
n
n
n
L
gi
ij
i
g
gi
i
i
i
i
P
P
B
P
P
B
B
(
6
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w
h
er
e
B
ij
,
B
oi
,
an
d
B
00
ar
e
to
d
eter
m
i
n
e
th
e
lo
s
s
co
ef
f
icie
n
t
m
atr
i
x
2
.
2
.
2
.
I
nequ
a
lity
co
ns
t
ra
int
A
p
o
w
er
li
m
it
co
n
s
tr
ain
t
is
u
s
ed
to
en
s
u
r
e
t
h
at
t
h
e
g
en
er
ati
n
g
u
n
i
t
o
p
er
ates
w
it
h
i
n
t
h
e
m
i
n
i
m
u
m
an
d
m
ax
i
m
u
m
li
m
it
s
.
T
h
is
co
n
s
tr
ai
n
t p
r
esen
ted
as
f
o
llo
w
:
m
i
n
m
a
x
ii
i
P
P
P
(
7
)
w
h
er
e
P
imin
an
d
P
imax
ar
e
m
i
n
i
m
u
m
a
n
d
m
a
x
i
m
u
m
g
en
er
ati
n
g
p
o
w
er
li
m
its
o
f
i
th
g
e
n
er
ati
n
g
u
n
it
s
.
3.
L
SA
A
L
G
O
RI
T
H
M
T
h
e
L
S
A
alg
o
r
it
h
m
i
s
a
n
e
w
m
eta
h
eu
r
i
s
tic
m
e
th
o
d
d
ev
elo
p
ed
in
2
0
1
5
b
y
[
2
0
]
.
T
h
is
m
et
h
o
d
ca
n
b
e
u
s
ed
to
o
p
ti
m
ize
co
m
p
le
x
n
o
n
li
n
ea
r
p
r
o
b
le
m
s
.
I
t
d
er
iv
ed
f
r
o
m
a
n
atu
r
al
li
g
h
tn
i
n
g
p
h
e
n
o
m
e
n
o
n
as
a
s
tep
lead
er
p
r
o
p
ag
atio
n
m
ec
h
an
is
m
[
2
4
]
.
Nea
r
b
y
t
h
e
th
u
n
d
er
clo
u
d
r
eg
io
n
ca
n
b
e
f
o
u
n
d
o
x
y
g
e
n
an
d
n
itro
g
e
n
an
d
h
y
d
r
o
g
en
m
o
lecu
le
s
.
Du
r
in
g
th
e
w
ater
m
o
lecu
le
s
ar
e
f
r
ee
zi
n
g
w
it
h
i
n
a
t
h
u
n
d
er
clo
u
d
,
p
ar
ts
o
f
w
ater
m
o
lecu
les
a
r
e
u
n
ab
le
to
f
it
t
h
e
ice
s
tr
u
ctu
r
e.
T
h
u
s
,
th
e
s
e
m
o
lecu
les
a
t
h
i
g
h
s
p
ee
d
s
w
i
ll
b
e
ej
ec
ted
f
r
o
m
t
h
e
f
o
r
m
i
n
g
ice.
T
h
er
ef
o
r
e,
th
e
h
y
d
r
o
g
e
n
an
d
o
x
y
g
e
n
at
o
m
s
ar
e
d
etac
h
ed
an
d
ej
ec
ted
r
an
d
o
m
l
y
i
n
d
if
f
er
en
t
d
i
r
ec
tio
n
s
as
p
r
o
j
ec
tiles
.
T
h
ese
p
r
o
j
ec
tiles
tr
av
el
o
v
er
th
e
at
m
o
s
p
h
er
e
an
d
s
tar
t
th
e
io
n
izatio
n
p
at
h
o
v
er
co
llis
io
n
a
n
d
tr
an
s
itio
n
i
n
to
th
e
s
tep
lead
er
.
I
n
th
is
p
r
o
p
o
s
ed
al
g
o
r
ith
m
,
ev
er
y
p
r
o
j
ec
tile
w
ill
cr
ea
te
a
s
tep
lead
e
r
o
r
ch
an
n
el
th
at
r
ep
r
esen
ts
th
e
in
itial p
o
p
u
latio
n
s
ize.
T
h
e
p
r
o
j
ec
tile c
o
n
ce
p
t in
th
is
al
g
o
r
it
h
m
i
s
h
ig
h
l
y
s
i
m
ilar
to
“
p
ar
ticle”
u
s
ed
in
P
SO.
T
h
e
p
r
o
j
ec
tiles
ar
e
tak
en
in
t
o
co
n
s
id
er
atio
n
w
h
ic
h
is
k
n
o
w
n
as
th
e
f
a
s
t
p
ar
ticles
in
v
o
l
v
e
m
e
n
t
is
r
ec
o
g
n
ized
in
t
h
e
s
tr
u
ctu
r
e
f
o
r
m
atio
n
o
f
th
e
b
i
n
ar
y
tr
ee
o
f
th
e
s
tep
lead
er
.
A
ls
o
in
t
h
e
f
o
r
m
atio
n
o
f
co
n
c
u
r
r
en
t
o
f
t
w
o
lead
er
tip
s
at
p
o
in
ts
o
f
t
h
e
f
o
r
k
in
s
tead
o
f
t
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ilizes
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[
2
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Sep
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3
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374
3
.
1
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1.
T
ra
ns
it
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n pro
j
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t
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T
h
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r
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p
e
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r
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s
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r
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m
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n
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er
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ir
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tio
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a
n
d
o
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h
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f
o
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o
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eled
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m
n
u
m
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er
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I
t c
an
b
e
d
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as f
o
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s
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or
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o
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r
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T
T
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x
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fx
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s
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m
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m
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[
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f
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3
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1
.
2
.
Sp
a
ce
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Sp
ac
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p
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ies
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s
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o
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n
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p
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f
r
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a
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f
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r
0
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xS
S
S
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fx
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(
9
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w
h
er
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x
S
p
r
ese
n
ts
a
r
a
n
d
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m
v
ar
iab
le.
T
h
e
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ca
tio
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d
ir
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t
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s
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ch
ar
g
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o
f
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h
ap
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p
ar
a
m
eter
μ
.
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h
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d
is
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et
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S
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d
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L
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n
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i
f
o
r
s
p
ec
if
ic
p
Si
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A
cc
o
r
d
in
g
to
th
i
s
d
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ip
tio
n
,
p
Si
p
o
s
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at
s
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is
g
i
v
e
n
as:
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xp
(
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i
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P
rand
(
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h
er
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ex
p
r
a
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is
a
n
ex
p
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e
n
tial
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cr
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ted
r
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d
o
m
l
y
.
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h
e
n
p
Si
h
as
a
n
e
g
ati
v
e
v
alu
e,
t
h
en
it
s
h
o
u
ld
s
u
b
tr
ac
t
t
h
e
p
r
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d
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ce
d
r
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d
o
m
n
u
m
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to
(
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at
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id
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o
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o
s
iti
v
e
v
al
u
es.
T
h
er
ef
o
r
e,
n
e
w
p
o
s
itio
n
p
Si
new
d
o
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o
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n
s
u
r
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s
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p
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r
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p
ag
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n
u
n
til it c
a
n
f
i
n
d
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g
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o
d
s
o
lu
tio
n
.
3
.
1
.
3
.
L
ea
d pro
j
ec
t
ile
T
h
e
lead
p
r
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j
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av
els
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e
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r
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ciate
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o
t
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en
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g
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lar
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g
s
ec
tio
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n
f
r
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n
t
o
f
t
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g
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h
er
ef
o
r
e,
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ca
n
o
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tain
f
r
o
m
n
o
r
m
a
l
d
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tr
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u
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m
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eled
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lea
d
p
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j
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tile a
s
a
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m
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m
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22
(
x
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2
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L
f
x
e
(
11
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h
er
e
μ
is
a
s
h
ap
in
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ar
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m
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ter
to
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p
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p
L
an
d
σ
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th
e
s
ca
lin
g
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ar
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eter
t
h
at
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r
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h
u
s
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d
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ea
s
in
g
ex
p
o
n
e
n
tial
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d
p
r
o
g
r
ess
to
d
is
co
v
er
th
e
b
est s
o
l
u
t
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n
.
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ased
o
n
th
i
s
id
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,
at
s
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th
e
p
L
p
o
s
itio
n
ca
n
b
e
w
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s
:
(
,
)
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n
e
w
L
L
P
p
n
o
r
m
r
a
n
d
(
12
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w
h
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n
o
r
mra
n
d
p
r
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ts
a
r
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n
d
o
m
n
u
m
b
er
p
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d
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ce
d
b
y
th
e
d
is
tr
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u
tio
n
f
u
n
ct
io
n
.
p
L
is
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n
u
p
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ated
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ca
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f
t
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tile
w
h
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L
n
e
w
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e
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e
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n
a
s
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m
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w
a
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f
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th
e
n
s
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er
w
i
ll
p
r
o
v
id
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to
a
n
e
w
lo
ca
tio
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.
4.
SI
M
UL
AT
I
O
N
R
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S
UL
T
S
I
n
th
i
s
r
esear
ch
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th
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A
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r
ith
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m
s
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s
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in
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4
.
1
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1
-
4
.
1
.
2
,
to
v
alid
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th
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p
r
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p
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s
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alg
o
r
ith
m
f
o
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p
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le
m
s
.
I
n
o
r
d
er
to
in
v
est
ig
ate
th
e
p
er
f
o
r
m
a
n
ce
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f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8938
Op
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h
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n
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f
r
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t
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p
r
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s
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t
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.
T
h
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p
ar
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eter
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th
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L
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A;
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ith
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in
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t
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.
4
.
1
.
1.
T
est
ca
s
e
1
Th
is
test
s
y
s
te
m
h
a
s
i
m
p
l
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m
en
ted
f
o
r
s
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p
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ap
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8
3
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4
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is
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s
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co
ef
f
icie
n
t
i
s
g
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e
n
in
[
2
5
]
.
T
h
e
co
s
t
f
u
n
ctio
n
w
i
th
th
e
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an
d
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[1
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[2
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.
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.
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[1
3
]
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.
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4
]
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–
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0
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8
.
[1
5
]
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.
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ian
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n
d
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.
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6
]
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.
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,
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8
.
[1
7
]
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.
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.
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o
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a
d
,
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lv
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b
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h
im
,
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8
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.
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9
]
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3
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Sep
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m
b
er
20
20
:
3
7
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378
378
[2
1
]
M
.
M
.
Isla
m
,
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S
h
a
re
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f
,
M
.
Na
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rial,
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Riz
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,
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.
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n
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.
Arti
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[2
2
]
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.
El
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n
d
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b
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e
lf
a
tt
a
h
,
“
Ne
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sig
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3
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4
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R.
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d
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Ok
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,
“
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p
.
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6
.
[2
5
]
T
.
Na
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m
M
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k
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.
u
l
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s
a
r,
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.
F
.
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e
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.
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k
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tar,
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n
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ro
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it
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ts,”
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tr.
Po
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Res
.
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.
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[2
6
]
C.
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.
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ro
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3
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p
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8
–
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5
,
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0
1
1
.
[2
7
]
P
.
K.
V
it
t
h
a
lad
e
v
u
n
i
a
n
d
M
.
-
S
.
A
lo
u
in
i,
“
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o
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s
.
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l.
4
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.
4
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p
.
4
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.
[2
8
]
Ke
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g
,
“
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sp
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tch
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”
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s
.
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1
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p
.
2
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5
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,
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0
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0
.
[2
9
]
L
.
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s
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n
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.
C.
M
a
rian
i,
“
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m
b
in
in
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o
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Dif
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lv
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t,
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E
T
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n
s.
Po
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l.
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.
3
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p
.
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–
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5
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.
[3
0
]
A
.
S
rin
iv
a
sa
Re
d
d
y
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n
d
K.
V
a
is
a
k
h
,
“
S
h
u
f
f
led
d
if
f
e
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n
ti
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ti
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f
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r
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sc
a
le
e
c
o
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isp
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tch
,
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c
tr.
Po
we
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Res
.
,
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l.
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6
,
p
p
.
2
3
7
–
2
4
5
,
2
0
1
3
.
[3
1
]
K.
Bh
a
tt
a
c
h
a
rjee
,
A
.
Bh
a
tt
a
c
h
a
ry
a
,
a
n
d
S
.
H.
N.
De
y
,
“
Op
p
o
siti
o
n
a
l
Re
a
l
Co
d
e
d
Ch
e
m
ica
l
Re
a
c
ti
o
n
Op
t
im
iza
ti
o
n
f
o
r
d
iff
e
re
n
t
e
c
o
n
o
m
ic d
isp
a
tch
p
ro
b
lem
s,”
In
t.
J
.
El
e
c
tr.
P
o
we
r E
n
e
rg
y
S
y
st.
,
v
o
l.
5
5
,
p
p
.
3
7
8
–
3
9
1
,
2
0
1
4
.
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
M
u
ra
d
Ya
h
y
a
Na
ss
a
r
re
c
e
iv
e
d
a
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
e
l
e
c
tri
c
a
l
e
n
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
,
Jo
h
o
r,
M
a
la
y
sia
in
2
0
1
9
,
w
h
e
re
h
e
is
p
u
rs
u
in
g
a
m
a
ste
r
'
s
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
.
He
r
re
se
a
rc
h
in
tere
st
i
n
c
lu
d
e
s
p
o
w
e
r
d
isp
a
tch
,
re
n
e
w
a
b
le
e
n
e
rg
y
so
u
rc
e
s
a
n
d
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
s.
Dr.
M
o
h
d
No
o
r
A
b
d
u
l
lah
re
c
e
iv
e
d
h
is
B.
En
g
.
(Ho
n
s)
i
n
El
e
c
tri
c
a
l
En
g
in
e
e
ri
n
g
a
n
d
M
.
En
g
.
i
n
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
(
P
o
w
e
r
S
y
ste
m
)
f
ro
m
Un
iv
e
rsiti
T
e
k
n
o
lo
g
i
M
a
lay
sia
(U
T
M
)
in
2
0
0
8
a
n
d
2
0
1
0
re
sp
e
c
ti
v
e
l
y
.
He
a
lso
re
c
e
i
v
e
d
a
P
h
.
D
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsit
y
o
f
M
a
la
y
a
(UM)
in
2
0
1
4
.
He
h
a
s
b
e
e
n
w
it
h
Un
iv
e
rsiti
T
u
n
Hu
ss
e
i
n
On
n
M
a
lay
sia
(UT
HM)
f
ro
m
2
0
0
8
t
o
2
0
1
4
a
s
a
tu
to
r
.
He
is
c
u
rre
n
tl
y
a
s
a
Lec
tu
re
r
in
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
P
o
w
e
r
En
g
in
e
e
rin
g
,
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
En
g
in
e
e
rin
g
(
F
KEE)
,
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
(
UTHM)
.
He
a
lso
a
p
p
o
i
n
ted
a
s a
h
e
a
d
o
f
G
r
e
e
n
a
n
d
S
u
sta
in
a
b
le E
n
e
rg
y
(
G
S
En
e
rg
y
)
F
o
c
u
s
G
ro
u
p
in
F
KEE,
UT
HM.
He
wa
s
a
m
e
m
b
e
r
o
f
Bo
a
rd
o
f
En
g
in
e
e
r
M
a
lay
sia
.
He
a
lso
a
c
e
rti
f
ied
o
f
q
u
a
li
f
ied
p
e
rso
n
o
f
S
EDA
M
a
la
y
sia
G
rid
Co
n
n
e
c
ted
P
h
o
t
o
v
o
lt
a
ic
S
y
ste
m
d
e
sig
n
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
e
lec
tri
c
p
o
w
e
r
d
isp
a
tch
,
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
,
re
n
e
w
a
b
le
e
n
e
rg
y
a
n
d
m
e
ta
-
h
e
u
risti
c
o
p
t
im
iza
ti
o
n
tec
h
n
iq
u
e
s.
A
si
f
A
h
m
e
d
re
c
e
iv
e
d
h
is
B.
En
g
.
in
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
a
n
d
M
.
En
g
.
in
El
e
c
tri
c
a
l
P
o
w
e
r
En
g
in
e
e
rin
g
f
ro
m
M
e
h
ra
n
Un
iv
e
rsity
Ja
m
sh
o
ro
,
P
a
k
is
tan
(
M
UET
)
in
2
0
1
4
a
n
d
2
0
1
8
re
sp
e
c
ti
v
e
l
y
.
He
is
c
u
rre
n
tl
y
p
u
rsu
in
g
h
is
P
h
.
D
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
f
ro
m
Un
iv
e
rsiti
T
u
n
Hu
ss
e
in
On
n
M
a
lay
sia
(U
T
HM).
He
h
a
s
b
e
e
n
w
it
h
In
d
u
s
Un
iv
e
rsity
Ka
ra
c
h
i
f
ro
m
F
e
b
-
2
0
1
7
to
Ja
n
-
2
0
1
9
a
s
a
Lec
tu
re
r
in
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
tec
h
n
o
lo
g
y
,
F
a
c
u
lt
y
o
f
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
(F
ES
T
).
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
p
o
w
e
r
d
istri
b
u
ti
o
n
a
n
d
g
e
n
e
ra
ti
o
n
,
p
o
w
e
r
e
lec
tro
n
ics
a
n
d
re
n
e
wa
b
le en
e
rg
y
.
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